Dataset Information

Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment.

ABSTRACT: Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox's proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by L(2) or L(1). This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are initially sensitive to chemotherapy. Net-Cox toolbox is available at http://compbio.cs.umn.edu/Net-Cox/.

SUBMITTER: Zhang W

PROVIDER: S-EPMC3605061 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment.

Zhang Wei W Ota Takayo T Shridhar Viji V Chien Jeremy J Wu Baolin B Kuang Rui R

PLoS computational biology 20130321 3

Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multipl ...[more]

PMID: 23555212

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Project description:Genomic technologies are reshaping the molecular landscape of colorectal cancer (CRC), revealing that oncogenic driver mutations (APC and TP53) coexist with still underappreciated genetic events. We hypothesized that mutational analysis of CRC-linked genes may provide novel information on the connection between genetically-deregulated pathways and clinical outcomes. We performed next-generation sequencing (NGS) analysis of 16 recurrently mutated genes in CRC exploiting tissue specimens from 98 advanced CRC patients. Multiple correspondence analysis (MCA) was used to identify gene sets characterizing negative and positive outliers (patients in the lowest and highest quartile of progression-free survival, PFS). Variables potentially affecting PFS and overall survival (OS) were tested in univariate and multivariate Cox proportional hazard models. Sensitivity analyses and resampling were used to assess the robustness of genomic predictors. MCA revealed that APC and TP53 mutations were close to the negative outlier group, whereas mutations in other WNT pathway genes were in proximity of the positive outliers. Reasoning that genetic alterations interact epistatically, producing greater or weaker consequences in combination than when individually considered, we tested whether patients whose tumors carried a genetic background characterized by APC and TP53 mutations without coexisting mutations in other WNT genes (AMER1, FBXW7, TCF7L2, CTNNB1, SOX9) had adverse survival outcomes. With this approach, we identified two oncodriver signatures (ODS1 and ODS2) associated with shorter PFS (ODS1 multivariate Cox for PFS: HR 2.16, 95%CI: 1.28-3.64, p?=?0.004; ODS2 multivariate Cox for PFS: HR 2.61, 95%CI: 1.49-4.58, p?=?0.001). Clinically-focused and molecularly-focused sensitivity analyses, resampling, and reclassification of mutations confirmed the stability of ODS1/2. Moreover, ODS1/2 negatively impacted OS. Collectively, our results point to co-occurring driver mutations as an adverse molecular factor in advanced CRC. This relationship depends on a broader genetic context highlighting the importance of genetic interactions.

Project description:BackgroundDiscovering robust markers for cancer prognosis based on gene expression data is an important yet challenging problem in translational bioinformatics. By integrating additional information in biological pathways or a protein-protein interaction (PPI) network, we can find better biomarkers that lead to more accurate and reproducible prognostic predictions. In fact, recent studies have shown that, "modular markers," that integrate multiple genes with potential interactions can improve disease classification and also provide better understanding of the disease mechanisms.ResultsIn this work, we propose a novel algorithm for finding robust and effective subnetwork markers that can accurately predict cancer prognosis. To simultaneously discover multiple synergistic subnetwork markers in a human PPI network, we build on our previous work that uses affinity propagation, an efficient clustering algorithm based on a message-passing scheme. Using affinity propagation, we identify potential subnetwork markers that consist of discriminative genes that display coherent expression patterns and whose protein products are closely located on the PPI network. Furthermore, we incorporate the topological information from the PPI network to evaluate the potential of a given set of proteins to be involved in a functional module. Primarily, we adopt widely made assumptions that densely connected subnetworks may likely be potential functional modules and that proteins that are not directly connected but interact with similar sets of other proteins may share similar functionalities.ConclusionsIncorporating topological attributes based on these assumptions can enhance the prediction of potential subnetwork markers. We evaluate the performance of the proposed subnetwork marker identification method by performing classification experiments using multiple independent breast cancer gene expression datasets and PPI networks. We show that our method leads to the discovery of robust subnetwork markers that can improve cancer classification.

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Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment.

Publications

Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment.

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